Heart Disease Prediction Using Statistical Feature Selection and Interpretable Machine Learning.

Heart disease is a major cause of death worldwide, making its early prediction an important clinical and computational issue. Several studies have addressed challenges such as data scarcity, feature selection, and model interpretability individually, but fewer studies have proposed an integrated framework that addresses these challenges in a synergistic manner. This paper presents a comprehensive predictive framework that uses: (1) a generative adversarial network (GAN) to address class imbalance and data scarcity; (2) a hybrid feature selection approach that combines statistical pre-filtering via Welch's t.-test and Cohen's d effect size, along with metaheuristic optimization via Harris Hawk Optimization; and (3) various explainable artificial intelligence methods, including SHAP, partial dependence plots, and odds ratios. This framework was assessed on the Cleveland and Statlog datasets, yielding strong accuracy, F1-scores, and ROC-AUC values compared with selected baselines and existing methods. The model provides a robust, interpretable computational framework for heart disease prediction, linking machine learning performance with clinical interpretability.